Learning a Gaussian Process Prior for Automatically Generating Music Playlists

This paper presents AutoDJ: a system for automatically generating music playlists based on one or more seed songs selected by a user. AutoDJ uses Gaussian Process Regression to learn a user preference function over songs. This function takes music metadata as inputs. This paper further introduces Kernel Meta-Training, which is a method of learning a Gaussian Process kernel from a distribution of functions that generates the learned function. For playlist generation, AutoDJ learns a kernel from a large set of albums. This learned kernel is shown to be more effective at predicting users' playlists than a reasonable hand-designed kernel.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: John C. Platt, Christopher J. C. Burges, S. Swenson, C. Weare, A. Zheng
• Keywords: Gaussian Process, Gaussian Process Kernel, Gaussian Process Regression, NIPS 2001, NIPS 2007